New article - A framework for augmenting human analytic power with machine learning in science education research in JRST


My co-authors Marcus Kubsch and Christina (Stina) Krist and I have been discussing and thinking about using machine learning in science education research—the primary field in which we work. The result of the many discussions (and projects!) we engaged in was this position paper in which we intend to spark a conversation about how we can use machine learning in ways that augment our work and capabilities as researchers—and not only to automate existing processes.

Thanks to Marcus, the paper is always available open-access here:

The abstract follows:

Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students’ work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers’ use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting-edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human-driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

Thanks to Marcus and Stina for this collaboration, one we’ve sustained over several years and spun off in new directions. In fact, Stina helped to spark my interest in machine learning mannny years back—when we both worked on a science education research project in graduate school and were motivated to use machine learning to make our work easier. Perhaps expectedly, machine learning did not do precisely that, but using machine learning did spark papers and projects on using these techniques in useful ways.

A reference for the paper is here:

Kubsch, M., Krist, C., & Rosenberg. J. M. (advance online publication). Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research. Journal of Research in Science Teaching.